Optimal distribution system reconfiguration incorporating distributed generation based on simplified network approach / Mohammad Al Samman

• 第一著者: Mohammad , Al Samman
• フォーマット: 学位論文
• 出版事項: 2020
• 主題: TK Electrical engineering. Electronics Nuclear engineering

Network Reconfiguration (NR) and Distributed Generation (DG) are well-accepted strategies to minimize power loss and voltage deviation in the Electrical Distribution Network (EDN). Since the NR problem contains a huge combinational search space, most researchers applied meta-heuristic methods to attain optimal NR solution. However, meta-heuristic methods do not always guarantee optimal solution and furthermore they consume huge processing time. This occurs mainly due to (1) random solution’s initialization and (2) the verification of solution in each iteration to fulfill the operation constraints during the optimization process. Besides, solving the NR problem simultaneously with DG placement and sizing increases the computational burden due to increase of the search space. With the aim of reducing the computational time and improving the consistency in obtaining the optimal solution as well as minimizing power loss and voltage deviation of the EDN, this work proposes a new method based on a two-stage optimization. This method introduces a technique to simplify the network into a simplified network graph. Then, the simplified network is utilized for guided initializations and generations of the population as well as for the proper population’s codification. The proposed method is employed to solve the NR problem and DG integration separately and simultaneously. In addition, this work considered non-dispatchable renewable energy resources and load variations for daily operation. The selected meta-heuristic techniques in this research involve the Firefly Algorithm (FA) and Biogeography-Based Optimization (BBO). To verify the efficiency of the proposed method, simulations were carried out on 33-bus, 69-bus, and 118-bus IEEE test systems. Furthermore, comparisons were performed between the proposed method along with the conventional evolutionary programming, particle swarm optimization, FA and BBO as well as the previous works. The obtained results of the NR problem as well as DG placement and sizing demonstrate the superiority of the proposed method in obtaining a fast and high-quality solution that minimize the power loss and voltage deviation in different case studies.